{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with Macro Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.477791Z",
     "start_time": "2020-06-17T19:35:25.922084Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.481546Z",
     "start_time": "2020-06-17T19:35:26.479309Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Variable   | Description                                  | Transformation     |\n",
    "|------------|----------------------------------------------|--------------------|\n",
    "| realgdp    | Real gross domestic product                  | Annual Growth Rate |\n",
    "| realcons   | Real personal consumption expenditures       | Annual Growth Rate |\n",
    "| realinv    | Real gross private domestic investment       | Annual Growth Rate |\n",
    "| realgovt   | Real federal expenditures & gross investment | Annual Growth Rate |\n",
    "| realdpi    | Real private disposable income               | Annual Growth Rate |\n",
    "| m1         | M1 nominal money stock                       | Annual Growth Rate |\n",
    "| tbilrate   | Monthly treasury bill rate                 | Level              |\n",
    "| unemp      | Seasonally adjusted unemployment rate (%)    | Level              |\n",
    "| infl       | Inflation rate                               | Level              |\n",
    "| realint    |  Real interest rate                          | Level              |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.502189Z",
     "start_time": "2020-06-17T19:35:26.482604Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 203 entries, 0 to 202\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   year      203 non-null    float64\n",
      " 1   quarter   203 non-null    float64\n",
      " 2   realgdp   203 non-null    float64\n",
      " 3   realcons  203 non-null    float64\n",
      " 4   realinv   203 non-null    float64\n",
      " 5   realgovt  203 non-null    float64\n",
      " 6   realdpi   203 non-null    float64\n",
      " 7   cpi       203 non-null    float64\n",
      " 8   m1        203 non-null    float64\n",
      " 9   tbilrate  203 non-null    float64\n",
      " 10  unemp     203 non-null    float64\n",
      " 11  pop       203 non-null    float64\n",
      " 12  infl      203 non-null    float64\n",
      " 13  realint   203 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 22.3 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame(sm.datasets.macrodata.load().data)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.514418Z",
     "start_time": "2020-06-17T19:35:26.503024Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain a binary target variable, we compute the 20-quarter rolling average of the annual growth rate of quarterly real GDP. We then assign 1 if current growth exceeds the moving average and 0 otherwise. Finally, we shift the indicator variables to align next quarter's outcome with the current quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.526503Z",
     "start_time": "2020-06-17T19:35:26.515329Z"
    }
   },
   "outputs": [],
   "source": [
    "data['growth_rate'] = data.realgdp.pct_change(4)\n",
    "data['target'] = (data.growth_rate > data.growth_rate.rolling(20).mean()).astype(int).shift(-1)\n",
    "data.quarter = data.quarter.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.532744Z",
     "start_time": "2020-06-17T19:35:26.527666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    112\n",
       "1.0     90\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.549027Z",
     "start_time": "2020-06-17T19:35:26.534887Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13324.600</td>\n",
       "      <td>9267.7</td>\n",
       "      <td>1990.693</td>\n",
       "      <td>991.551</td>\n",
       "      <td>9838.3</td>\n",
       "      <td>216.889</td>\n",
       "      <td>1474.7</td>\n",
       "      <td>1.17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>305.270</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>4</td>\n",
       "      <td>13141.920</td>\n",
       "      <td>9195.3</td>\n",
       "      <td>1857.661</td>\n",
       "      <td>1007.273</td>\n",
       "      <td>9920.4</td>\n",
       "      <td>212.174</td>\n",
       "      <td>1576.5</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.9</td>\n",
       "      <td>305.952</td>\n",
       "      <td>-8.79</td>\n",
       "      <td>8.91</td>\n",
       "      <td>-0.018619</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12925.410</td>\n",
       "      <td>9209.2</td>\n",
       "      <td>1558.494</td>\n",
       "      <td>996.287</td>\n",
       "      <td>9926.4</td>\n",
       "      <td>212.671</td>\n",
       "      <td>1592.8</td>\n",
       "      <td>0.22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>306.547</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.033026</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12901.504</td>\n",
       "      <td>9189.0</td>\n",
       "      <td>1456.678</td>\n",
       "      <td>1023.528</td>\n",
       "      <td>10077.5</td>\n",
       "      <td>214.469</td>\n",
       "      <td>1653.6</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.2</td>\n",
       "      <td>307.226</td>\n",
       "      <td>3.37</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-0.038297</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12990.341</td>\n",
       "      <td>9256.0</td>\n",
       "      <td>1486.398</td>\n",
       "      <td>1044.088</td>\n",
       "      <td>10040.6</td>\n",
       "      <td>216.385</td>\n",
       "      <td>1673.9</td>\n",
       "      <td>0.12</td>\n",
       "      <td>9.6</td>\n",
       "      <td>308.013</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-0.025086</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  quarter    realgdp  realcons   realinv  realgovt  realdpi  \\\n",
       "198  2008.0        3  13324.600    9267.7  1990.693   991.551   9838.3   \n",
       "199  2008.0        4  13141.920    9195.3  1857.661  1007.273   9920.4   \n",
       "200  2009.0        1  12925.410    9209.2  1558.494   996.287   9926.4   \n",
       "201  2009.0        2  12901.504    9189.0  1456.678  1023.528  10077.5   \n",
       "202  2009.0        3  12990.341    9256.0  1486.398  1044.088  10040.6   \n",
       "\n",
       "         cpi      m1  tbilrate  unemp      pop  infl  realint  growth_rate  \\\n",
       "198  216.889  1474.7      1.17    6.0  305.270 -3.16     4.33     0.000262   \n",
       "199  212.174  1576.5      0.12    6.9  305.952 -8.79     8.91    -0.018619   \n",
       "200  212.671  1592.8      0.22    8.1  306.547  0.94    -0.71    -0.033026   \n",
       "201  214.469  1653.6      0.18    9.2  307.226  3.37    -3.19    -0.038297   \n",
       "202  216.385  1673.9      0.12    9.6  308.013  3.56    -3.44    -0.025086   \n",
       "\n",
       "     target  \n",
       "198     0.0  \n",
       "199     0.0  \n",
       "200     0.0  \n",
       "201     0.0  \n",
       "202     NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.564550Z",
     "start_time": "2020-06-17T19:35:26.549859Z"
    }
   },
   "outputs": [],
   "source": [
    "pct_cols = ['realcons', 'realinv', 'realgovt', 'realdpi', 'm1']\n",
    "drop_cols = ['year', 'realgdp', 'pop', 'cpi', 'growth_rate']\n",
    "data.loc[:, pct_cols] = data.loc[:, pct_cols].pct_change(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.577015Z",
     "start_time": "2020-06-17T19:35:26.565532Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data.drop(drop_cols, axis=1), columns=['quarter'], drop_first=True).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.596900Z",
     "start_time": "2020-06-17T19:35:26.578172Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>m1</th>\n",
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       "      <th>4</th>\n",
       "      <td>0.036957</td>\n",
       "      <td>0.156237</td>\n",
       "      <td>-0.016692</td>\n",
       "      <td>0.036356</td>\n",
       "      <td>-0.000716</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.034147</td>\n",
       "      <td>-0.040877</td>\n",
       "      <td>-0.043426</td>\n",
       "      <td>0.024170</td>\n",
       "      <td>-0.010586</td>\n",
       "      <td>2.68</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.019409</td>\n",
       "      <td>0.024718</td>\n",
       "      <td>-0.033758</td>\n",
       "      <td>0.026821</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>2.36</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.70</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.019673</td>\n",
       "      <td>-0.132257</td>\n",
       "      <td>-0.015738</td>\n",
       "      <td>0.018278</td>\n",
       "      <td>0.007857</td>\n",
       "      <td>2.29</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.009715</td>\n",
       "      <td>-0.196903</td>\n",
       "      <td>0.029544</td>\n",
       "      <td>0.014830</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>2.37</td>\n",
       "      <td>6.8</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   realcons   realinv  realgovt   realdpi        m1  tbilrate  unemp  infl  \\\n",
       "4  0.036957  0.156237 -0.016692  0.036356 -0.000716      3.50    5.2  2.31   \n",
       "5  0.034147 -0.040877 -0.043426  0.024170 -0.010586      2.68    5.2  0.14   \n",
       "6  0.019409  0.024718 -0.033758  0.026821  0.002847      2.36    5.6  2.70   \n",
       "7  0.019673 -0.132257 -0.015738  0.018278  0.007857      2.29    6.3  1.21   \n",
       "8  0.009715 -0.196903  0.029544  0.014830  0.017908      2.37    6.8 -0.40   \n",
       "\n",
       "   realint  target  quarter_2  quarter_3  quarter_4  \n",
       "4     1.19     0.0          0          0          0  \n",
       "5     2.55     0.0          1          0          0  \n",
       "6    -0.34     0.0          0          1          0  \n",
       "7     1.08     0.0          0          0          1  \n",
       "8     2.77     0.0          0          0          0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.609222Z",
     "start_time": "2020-06-17T19:35:26.597742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 198 entries, 4 to 201\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   realcons   198 non-null    float64\n",
      " 1   realinv    198 non-null    float64\n",
      " 2   realgovt   198 non-null    float64\n",
      " 3   realdpi    198 non-null    float64\n",
      " 4   m1         198 non-null    float64\n",
      " 5   tbilrate   198 non-null    float64\n",
      " 6   unemp      198 non-null    float64\n",
      " 7   infl       198 non-null    float64\n",
      " 8   realint    198 non-null    float64\n",
      " 9   target     198 non-null    float64\n",
      " 10  quarter_2  198 non-null    uint8  \n",
      " 11  quarter_3  198 non-null    uint8  \n",
      " 12  quarter_4  198 non-null    uint8  \n",
      "dtypes: float64(10), uint8(3)\n",
      "memory usage: 17.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use an intercept and convert the quarter values to dummy variables and train the logistic regression model as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This produces the following summary for our model with 198 observations and 13 variables, including intercept:\n",
    "The summary indicates that the model has been trained using maximum likelihood and provides the maximized value of the log-likelihood function at -67.9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:26.686366Z",
     "start_time": "2020-06-17T19:35:26.610133Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.342965\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>target</td>      <th>  No. Observations:  </th>  <td>   198</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   185</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    12</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 17 Jun 2020</td> <th>  Pseudo R-squ.:     </th>  <td>0.5022</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:35:26</td>     <th>  Log-Likelihood:    </th> <td> -67.907</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -136.42</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>2.375e-23</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>   -8.5881</td> <td>    1.908</td> <td>   -4.502</td> <td> 0.000</td> <td>  -12.327</td> <td>   -4.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realcons</th>  <td>  130.1446</td> <td>   26.633</td> <td>    4.887</td> <td> 0.000</td> <td>   77.945</td> <td>  182.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realinv</th>   <td>   18.8414</td> <td>    4.053</td> <td>    4.648</td> <td> 0.000</td> <td>   10.897</td> <td>   26.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realgovt</th>  <td>  -19.0318</td> <td>    6.010</td> <td>   -3.166</td> <td> 0.002</td> <td>  -30.812</td> <td>   -7.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realdpi</th>   <td>  -52.2473</td> <td>   19.912</td> <td>   -2.624</td> <td> 0.009</td> <td>  -91.275</td> <td>  -13.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m1</th>        <td>   -1.3462</td> <td>    6.177</td> <td>   -0.218</td> <td> 0.827</td> <td>  -13.453</td> <td>   10.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tbilrate</th>  <td>   60.8607</td> <td>   44.350</td> <td>    1.372</td> <td> 0.170</td> <td>  -26.063</td> <td>  147.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unemp</th>     <td>    0.9487</td> <td>    0.249</td> <td>    3.818</td> <td> 0.000</td> <td>    0.462</td> <td>    1.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>infl</th>      <td>  -60.9647</td> <td>   44.362</td> <td>   -1.374</td> <td> 0.169</td> <td> -147.913</td> <td>   25.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realint</th>   <td>  -61.0453</td> <td>   44.359</td> <td>   -1.376</td> <td> 0.169</td> <td> -147.987</td> <td>   25.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quarter_2</th> <td>    0.1128</td> <td>    0.618</td> <td>    0.182</td> <td> 0.855</td> <td>   -1.099</td> <td>    1.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quarter_3</th> <td>   -0.1991</td> <td>    0.609</td> <td>   -0.327</td> <td> 0.744</td> <td>   -1.393</td> <td>    0.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quarter_4</th> <td>    0.0007</td> <td>    0.608</td> <td>    0.001</td> <td> 0.999</td> <td>   -1.191</td> <td>    1.192</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   No. Observations:                  198\n",
       "Model:                          Logit   Df Residuals:                      185\n",
       "Method:                           MLE   Df Model:                           12\n",
       "Date:                Wed, 17 Jun 2020   Pseudo R-squ.:                  0.5022\n",
       "Time:                        15:35:26   Log-Likelihood:                -67.907\n",
       "converged:                       True   LL-Null:                       -136.42\n",
       "Covariance Type:            nonrobust   LLR p-value:                 2.375e-23\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -8.5881      1.908     -4.502      0.000     -12.327      -4.849\n",
       "realcons     130.1446     26.633      4.887      0.000      77.945     182.344\n",
       "realinv       18.8414      4.053      4.648      0.000      10.897      26.786\n",
       "realgovt     -19.0318      6.010     -3.166      0.002     -30.812      -7.252\n",
       "realdpi      -52.2473     19.912     -2.624      0.009     -91.275     -13.220\n",
       "m1            -1.3462      6.177     -0.218      0.827     -13.453      10.761\n",
       "tbilrate      60.8607     44.350      1.372      0.170     -26.063     147.784\n",
       "unemp          0.9487      0.249      3.818      0.000       0.462       1.436\n",
       "infl         -60.9647     44.362     -1.374      0.169    -147.913      25.984\n",
       "realint      -61.0453     44.359     -1.376      0.169    -147.987      25.896\n",
       "quarter_2      0.1128      0.618      0.182      0.855      -1.099       1.325\n",
       "quarter_3     -0.1991      0.609     -0.327      0.744      -1.393       0.995\n",
       "quarter_4      0.0007      0.608      0.001      0.999      -1.191       1.192\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.Logit(data.target, sm.add_constant(data.drop('target', axis=1)))\n",
    "result = model.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LL-Null value of -136.42 is the result of the maximized log-likelihood function when only an intercept is included. It forms the basis for the pseudo-R2 statistic and the Log-Likelihood Ratio (LLR) test. \n",
    "The pseudo-R2 statistic is a substitute for the familiar R2 available under least squares. It is computed based on the ratio of the maximized log-likelihood function for the null model m0 and the full model m1 as follows:\n",
    "The values vary from 0 (when the model does not improve the likelihood) to 1 where the model fits perfectly and the log-likelihood is maximized at 0. Consequently, higher values indicate a better fit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T19:35:27.213739Z",
     "start_time": "2020-06-17T19:35:26.687389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('figure', figsize=(12, 7))\n",
    "plt.text(0.01, 0.05, str(result.summary()), {'fontsize': 14}, fontproperties = 'monospace')\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.1)\n",
    "plt.savefig('logistic_example.png', bbox_inches='tight', dpi=300);"
   ]
  }
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